A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

Du, Dangdang; Jia, Xiaofeng; Hao, Chaobo

doi:10.1155/2016/4615903

Cited by 18 publications

(12 citation statements)

References 19 publications

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“…This limitation leads to an increase in the training time and reduced prediction accuracy, especially for industrial datasets, which generally contain explosions of data, imbalanced distribution, and heteroscedasticity [ 43 ]. While a single LSSVM with reconstructed input samples and optimum parameters has excellent predictive efficiency under some conditions, it may have certain kinds of inherent bias in other cases due to its fixed kernel feature [ 44 ]. Artificial neural network (ANN) is a part of artificial intelligence.…”

Section: Artificial Intelligence and Its Application In Shear Strementioning

confidence: 99%

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sharma

Ahmed

Naseem

et al. 2021

Sensors

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Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

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Section: Artificial Intelligence and Its Application In Shear Strementioning

confidence: 99%

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sharma

Ahmed

Naseem

et al. 2021

Sensors

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show abstract

“…Therefore, we can obtain the LSSVR regression model by solving the following optimization problem, where γ is a constant similar to C in the standard SVR, (xi) is the mapping to the high dimensional feature space as in SVR, and ei ∈ R are the error variables. Even though, the performance of LSSVR degrades if the time series data have chaotic characteristics [17].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Energy time series forecasting-analytical and empirical assessment of conventional and machine learning models

Hamdoun

Sagheer

Youness

2021

IFS

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Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Recently, deep learning methods have been emerged in the artificial intelligence field attaining astonishing performance in a wide range of applications. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. Most of the review articles that handle the energy TSF problem are systematic reviews, however, a qualitative and quantitative study for the energy TSF problem is not yet available in the literature. The purpose of this paper is twofold, first it provides a comprehensive analytical assessment for conventional, machine learning, and deep learning methods that can be utilized to solve various energy TSF problems. Second, the paper carries out an empirical assessment for many selected methods through three real-world datasets. These datasets related to electrical energy consumption problem, natural gas problem, and electric power consumption of an individual household problem. The first two problems are univariate TSF and the third problem is a multivariate TSF. Compared to both conventional and machine learning contenders, the deep learning methods attain a significant improvement in terms of accuracy and forecasting horizons examined. In the meantime, their computational requirements are notably greater than other contenders. Eventually, the paper identifies a number of challenges, potential research directions, and recommendations to the research community may serve as a basis for further research in the energy forecasting domain.

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“…However, it is a widely known fact that the classification accuracy of LSSVM is highly influenced by the optimal selection of the kernel function and regularisation parameter. In this paper, the frequently used kernel function, the Radial Basis Function (RBF) was selected due to its excellent general performance, wider convergence domain, high-resolution power and requires fewer parameters (Keerthi and Lin 2003;Du et al 2016;Wang et al 2018). The RBF kernel function is expressed in Equation ( 26).…”

Section:  mentioning

confidence: 99%

Hybrid Intelligent Predictive Maintenance Model for Multiclass Fault Classification

Buabeng

Simons

Frimpong

et al. 2021

Preprint

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Data recorded from monitoring the health condition of industrial equipment are often high-dimensional, nonlinear, nonstationary and characterised by high levels of uncertainty. These factors limit the efficiency of machine learning techniques to produce desirable results when developing effective fault classification frameworks. This paper sought to propose a hybrid artificial intelligent predictive maintenance model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LSSVM) optimised by the combination of Coupled Simulated Annealing and Nelder-Mead Simplex optimisation algorithms (ICEEMDAN-PCA-LSSVM). Here, ICEEMDAN was first employed as a denoising technique to decompose signals into series of Intrinsic Mode Functions (IMFs) of which only relevant IMFs containing the relevant fault features were retained for signal reconstruction. PCA was then employed as a dimension reduction technique through which the resulting set of uncorrelated features extracted served as input for LSSVM for classifying various fault types. The proposed technique is compared with three established methods (Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Artificial Neural Network (ANN)) with multiclass classification capabilities. The various techniques were tested on an experimental UCI machine learning benchmark data obtained from multi-sensors of a hydraulic test rig. The results from the analysis revealed that the proposed ICEEMDAN-PCA-LSSVM technique is versatile and outperformed all the compared classifiers in terms of accuracy, error rate and other evaluation metrics considered. The proposed hybrid technique drastically reduced the redundancies and the dimension of features, allowing for the efficient consideration of relevant features for the enhancement of classification accuracy and convergence speed.

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A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

Cited by 18 publications

References 19 publications

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Energy time series forecasting-analytical and empirical assessment of conventional and machine learning models

Hybrid Intelligent Predictive Maintenance Model for Multiclass Fault Classification

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